Overview

Dataset statistics

Number of variables13
Number of observations18249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric9
Categorical2
Text1

Alerts

4046 has 242 (1.3%) zerosZeros
4770 has 5497 (30.1%) zerosZeros
Large Bags has 2370 (13.0%) zerosZeros
XLarge Bags has 12048 (66.0%) zerosZeros

Reproduction

Analysis started2024-02-16 20:27:17.420353
Analysis finished2024-02-16 20:27:21.846328
Duration4.43 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Date
Date

Distinct169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size285.1 KiB
Minimum2015-01-04 00:00:00
Maximum2018-03-25 00:00:00
2024-02-16T23:27:21.915995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.995133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AveragePrice
Real number (ℝ)

Distinct259
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4059784
Minimum0.44
Maximum3.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.052057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile0.83
Q11.1
median1.37
Q31.66
95-th percentile2.11
Maximum3.25
Range2.81
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.40267656
Coefficient of variation (CV)0.28640309
Kurtosis0.32519585
Mean1.4059784
Median Absolute Deviation (MAD)0.28
Skewness0.58030274
Sum25657.7
Variance0.16214841
MonotonicityNot monotonic
2024-02-16T23:27:22.101317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.15 202
 
1.1%
1.18 199
 
1.1%
1.08 194
 
1.1%
1.26 193
 
1.1%
1.13 192
 
1.1%
0.98 189
 
1.0%
1.19 188
 
1.0%
1.36 187
 
1.0%
1.59 186
 
1.0%
1.43 185
 
1.0%
Other values (249) 16334
89.5%
ValueCountFrequency (%)
0.44 1
 
< 0.1%
0.46 1
 
< 0.1%
0.48 1
 
< 0.1%
0.49 2
 
< 0.1%
0.51 5
< 0.1%
0.52 3
 
< 0.1%
0.53 6
< 0.1%
0.54 7
< 0.1%
0.55 3
 
< 0.1%
0.56 12
0.1%
ValueCountFrequency (%)
3.25 1
< 0.1%
3.17 1
< 0.1%
3.12 1
< 0.1%
3.05 1
< 0.1%
3.04 1
< 0.1%
3.03 1
< 0.1%
3 2
< 0.1%
2.99 2
< 0.1%
2.97 1
< 0.1%
2.96 1
< 0.1%

Total Volume
Real number (ℝ)

Distinct18237
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850644.01
Minimum84.56
Maximum62505647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.151124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum84.56
5-th percentile2371.862
Q110838.58
median107376.76
Q3432962.29
95-th percentile3716315.4
Maximum62505647
Range62505562
Interquartile range (IQR)422123.71

Descriptive statistics

Standard deviation3453545.4
Coefficient of variation (CV)4.0599185
Kurtosis92.104458
Mean850644.01
Median Absolute Deviation (MAD)102962.47
Skewness9.0076875
Sum1.5523403 × 1010
Variance1.1926976 × 1013
MonotonicityNot monotonic
2024-02-16T23:27:22.205223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4103.97 2
 
< 0.1%
3529.44 2
 
< 0.1%
46602.16 2
 
< 0.1%
13234.04 2
 
< 0.1%
3713.49 2
 
< 0.1%
19634.24 2
 
< 0.1%
3288.85 2
 
< 0.1%
9465.99 2
 
< 0.1%
2038.99 2
 
< 0.1%
2858.31 2
 
< 0.1%
Other values (18227) 18229
99.9%
ValueCountFrequency (%)
84.56 1
< 0.1%
379.82 1
< 0.1%
385.55 1
< 0.1%
419.98 1
< 0.1%
472.82 1
< 0.1%
482.26 1
< 0.1%
515.01 1
< 0.1%
530.96 1
< 0.1%
542.85 1
< 0.1%
561.1 1
< 0.1%
ValueCountFrequency (%)
62505646.52 1
< 0.1%
61034457.1 1
< 0.1%
52288697.89 1
< 0.1%
47293921.6 1
< 0.1%
46324529.7 1
< 0.1%
44655461.51 1
< 0.1%
43409835.75 1
< 0.1%
43167806.09 1
< 0.1%
42939821.55 1
< 0.1%
42867608.54 1
< 0.1%

4046
Real number (ℝ)

ZEROS 

Distinct17702
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293008.42
Minimum0
Maximum22743616
Zeros242
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.318583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.6
Q1854.07
median8645.3
Q3111020.2
95-th percentile1263359.7
Maximum22743616
Range22743616
Interquartile range (IQR)110166.13

Descriptive statistics

Standard deviation1264989.1
Coefficient of variation (CV)4.3172447
Kurtosis86.809113
Mean293008.42
Median Absolute Deviation (MAD)8616.69
Skewness8.6482198
Sum5.3471107 × 109
Variance1.6001974 × 1012
MonotonicityNot monotonic
2024-02-16T23:27:22.431077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242
 
1.3%
3 10
 
0.1%
4 8
 
< 0.1%
1.24 8
 
< 0.1%
1 8
 
< 0.1%
1.25 7
 
< 0.1%
6 7
 
< 0.1%
1.21 6
 
< 0.1%
1.3 5
 
< 0.1%
1.27 5
 
< 0.1%
Other values (17692) 17943
98.3%
ValueCountFrequency (%)
0 242
1.3%
1 8
 
< 0.1%
1.13 1
 
< 0.1%
1.19 3
 
< 0.1%
1.2 1
 
< 0.1%
1.21 6
 
< 0.1%
1.22 5
 
< 0.1%
1.23 1
 
< 0.1%
1.24 8
 
< 0.1%
1.25 7
 
< 0.1%
ValueCountFrequency (%)
22743616.17 1
< 0.1%
21620180.9 1
< 0.1%
18933038.04 1
< 0.1%
17787611.93 1
< 0.1%
17076650.82 1
< 0.1%
16573573.78 1
< 0.1%
16529797.6 1
< 0.1%
16383685.07 1
< 0.1%
16215328.75 1
< 0.1%
16000107.8 1
< 0.1%

4225
Real number (ℝ)

Distinct18103
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295154.57
Minimum0
Maximum20470573
Zeros61
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.530310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile103.614
Q13008.78
median29061.02
Q3150206.86
95-th percentile1303657.7
Maximum20470573
Range20470573
Interquartile range (IQR)147198.08

Descriptive statistics

Standard deviation1204120.4
Coefficient of variation (CV)4.0796265
Kurtosis91.949022
Mean295154.57
Median Absolute Deviation (MAD)28521.3
Skewness8.9424656
Sum5.3862757 × 109
Variance1.4499059 × 1012
MonotonicityNot monotonic
2024-02-16T23:27:22.671361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
0.3%
177.87 3
 
< 0.1%
215.36 3
 
< 0.1%
1.3 3
 
< 0.1%
1.26 3
 
< 0.1%
94.74 3
 
< 0.1%
13.6 2
 
< 0.1%
20.32 2
 
< 0.1%
35898.69 2
 
< 0.1%
6973.51 2
 
< 0.1%
Other values (18093) 18165
99.5%
ValueCountFrequency (%)
0 61
0.3%
1.26 3
 
< 0.1%
1.28 2
 
< 0.1%
1.3 3
 
< 0.1%
1.31 1
 
< 0.1%
1.32 2
 
< 0.1%
1.64 1
 
< 0.1%
2.39 1
 
< 0.1%
2.4 1
 
< 0.1%
2.48 1
 
< 0.1%
ValueCountFrequency (%)
20470572.61 1
< 0.1%
20445501.03 1
< 0.1%
20328161.55 1
< 0.1%
18956479.74 1
< 0.1%
17896391.6 1
< 0.1%
16602589.04 1
< 0.1%
16054083.86 1
< 0.1%
15899858.37 1
< 0.1%
14888077.69 1
< 0.1%
14437190.03 1
< 0.1%

4770
Real number (ℝ)

ZEROS 

Distinct12071
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22839.736
Minimum0
Maximum2546439.1
Zeros5497
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.735529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median184.99
Q36243.42
95-th percentile106156.57
Maximum2546439.1
Range2546439.1
Interquartile range (IQR)6243.42

Descriptive statistics

Standard deviation107464.07
Coefficient of variation (CV)4.7051362
Kurtosis132.56344
Mean22839.736
Median Absolute Deviation (MAD)184.99
Skewness10.159396
Sum4.1680234 × 108
Variance1.1548526 × 1010
MonotonicityNot monotonic
2024-02-16T23:27:22.808012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5497
30.1%
2.66 7
 
< 0.1%
3.32 7
 
< 0.1%
10.97 6
 
< 0.1%
1.59 6
 
< 0.1%
1.64 6
 
< 0.1%
1.6 6
 
< 0.1%
2.74 5
 
< 0.1%
1.66 5
 
< 0.1%
1.18 5
 
< 0.1%
Other values (12061) 12699
69.6%
ValueCountFrequency (%)
0 5497
30.1%
0.83 1
 
< 0.1%
1 3
 
< 0.1%
1.01 1
 
< 0.1%
1.09 1
 
< 0.1%
1.11 1
 
< 0.1%
1.12 1
 
< 0.1%
1.15 1
 
< 0.1%
1.16 1
 
< 0.1%
1.18 5
 
< 0.1%
ValueCountFrequency (%)
2546439.11 1
< 0.1%
1993645.36 1
< 0.1%
1896149.5 1
< 0.1%
1880231.38 1
< 0.1%
1811090.71 1
< 0.1%
1800065.57 1
< 0.1%
1773088.87 1
< 0.1%
1770948.09 1
< 0.1%
1761343.08 1
< 0.1%
1753852.61 1
< 0.1%

Total Bags
Real number (ℝ)

Distinct18097
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239639.2
Minimum0
Maximum19373134
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:22.879061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile628.89
Q15088.64
median39743.83
Q3110783.37
95-th percentile1005478.9
Maximum19373134
Range19373134
Interquartile range (IQR)105694.73

Descriptive statistics

Standard deviation986242.4
Coefficient of variation (CV)4.1155303
Kurtosis112.27216
Mean239639.2
Median Absolute Deviation (MAD)37299.96
Skewness9.7560717
Sum4.3731758 × 109
Variance9.7267407 × 1011
MonotonicityNot monotonic
2024-02-16T23:27:22.969001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.1%
990 5
 
< 0.1%
300 5
 
< 0.1%
550 4
 
< 0.1%
266.67 4
 
< 0.1%
916.67 4
 
< 0.1%
286.67 3
 
< 0.1%
263.33 3
 
< 0.1%
196.67 3
 
< 0.1%
260 3
 
< 0.1%
Other values (18087) 18200
99.7%
ValueCountFrequency (%)
0 15
0.1%
3.09 1
 
< 0.1%
3.11 1
 
< 0.1%
3.19 1
 
< 0.1%
3.33 1
 
< 0.1%
6.14 1
 
< 0.1%
6.18 1
 
< 0.1%
6.24 1
 
< 0.1%
6.36 1
 
< 0.1%
7.02 1
 
< 0.1%
ValueCountFrequency (%)
19373134.37 1
< 0.1%
16394524.11 1
< 0.1%
16298296.29 1
< 0.1%
15972492.07 1
< 0.1%
15804696.31 1
< 0.1%
15102426.94 1
< 0.1%
15051877.14 1
< 0.1%
14894893.8 1
< 0.1%
14504209.37 1
< 0.1%
14440611.5 1
< 0.1%

Small Bags
Real number (ℝ)

Distinct17321
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182194.69
Minimum0
Maximum13384587
Zeros159
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:23.071531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256.67
Q12849.42
median26362.82
Q383337.67
95-th percentile768147.23
Maximum13384587
Range13384587
Interquartile range (IQR)80488.25

Descriptive statistics

Standard deviation746178.51
Coefficient of variation (CV)4.095501
Kurtosis107.01289
Mean182194.69
Median Absolute Deviation (MAD)25599.49
Skewness9.54066
Sum3.3248708 × 109
Variance5.5678238 × 1011
MonotonicityNot monotonic
2024-02-16T23:27:23.175066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159
 
0.9%
203.33 11
 
0.1%
223.33 10
 
0.1%
533.33 10
 
0.1%
123.33 8
 
< 0.1%
196.67 8
 
< 0.1%
70 8
 
< 0.1%
103.33 8
 
< 0.1%
216.67 8
 
< 0.1%
20 8
 
< 0.1%
Other values (17311) 18011
98.7%
ValueCountFrequency (%)
0 159
0.9%
2.52 1
 
< 0.1%
2.57 1
 
< 0.1%
2.73 1
 
< 0.1%
2.79 1
 
< 0.1%
2.95 3
 
< 0.1%
2.96 1
 
< 0.1%
3.06 1
 
< 0.1%
3.09 1
 
< 0.1%
3.11 1
 
< 0.1%
ValueCountFrequency (%)
13384586.8 1
< 0.1%
12567155.58 1
< 0.1%
12540327.19 1
< 0.1%
11712807.19 1
< 0.1%
11392828.89 1
< 0.1%
11228049.63 1
< 0.1%
11112405.61 1
< 0.1%
10844852.22 1
< 0.1%
10832907.44 1
< 0.1%
10666942.78 1
< 0.1%

Large Bags
Real number (ℝ)

ZEROS 

Distinct15082
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54338.088
Minimum0
Maximum5719096.6
Zeros2370
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:23.235227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127.47
median2647.71
Q322029.25
95-th percentile195699.77
Maximum5719096.6
Range5719096.6
Interquartile range (IQR)21901.78

Descriptive statistics

Standard deviation243965.96
Coefficient of variation (CV)4.4897782
Kurtosis117.99948
Mean54338.088
Median Absolute Deviation (MAD)2647.71
Skewness9.7964546
Sum9.9161577 × 108
Variance5.9519392 × 1010
MonotonicityNot monotonic
2024-02-16T23:27:23.300306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2370
 
13.0%
3.33 187
 
1.0%
6.67 78
 
0.4%
10 47
 
0.3%
4.44 38
 
0.2%
13.33 28
 
0.2%
16.67 18
 
0.1%
26.67 18
 
0.1%
6.66 18
 
0.1%
20 14
 
0.1%
Other values (15072) 15433
84.6%
ValueCountFrequency (%)
0 2370
13.0%
0.97 1
 
< 0.1%
1.3 1
 
< 0.1%
1.33 1
 
< 0.1%
1.38 2
 
< 0.1%
1.44 1
 
< 0.1%
1.48 1
 
< 0.1%
1.55 1
 
< 0.1%
1.56 1
 
< 0.1%
1.62 1
 
< 0.1%
ValueCountFrequency (%)
5719096.61 1
< 0.1%
4324231.19 1
< 0.1%
4081397.72 1
< 0.1%
4023485.04 1
< 0.1%
3988101.74 1
< 0.1%
3917569.95 1
< 0.1%
3789722.9 1
< 0.1%
3618270.75 1
< 0.1%
3544729.39 1
< 0.1%
3434846.78 1
< 0.1%

XLarge Bags
Real number (ℝ)

ZEROS 

Distinct5588
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3106.4265
Minimum0
Maximum551693.65
Zeros12048
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:23.352990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3132.5
95-th percentile12058.452
Maximum551693.65
Range551693.65
Interquartile range (IQR)132.5

Descriptive statistics

Standard deviation17692.895
Coefficient of variation (CV)5.6955781
Kurtosis233.60261
Mean3106.4265
Median Absolute Deviation (MAD)0
Skewness13.139751
Sum56689177
Variance3.1303852 × 108
MonotonicityNot monotonic
2024-02-16T23:27:23.410251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12048
66.0%
3.33 29
 
0.2%
6.67 16
 
0.1%
1.11 15
 
0.1%
5 12
 
0.1%
10 9
 
< 0.1%
16.67 8
 
< 0.1%
2.22 7
 
< 0.1%
150 6
 
< 0.1%
13.33 6
 
< 0.1%
Other values (5578) 6093
33.4%
ValueCountFrequency (%)
0 12048
66.0%
1 1
 
< 0.1%
1.11 15
 
0.1%
1.26 1
 
< 0.1%
1.3 1
 
< 0.1%
1.38 1
 
< 0.1%
1.41 2
 
< 0.1%
1.45 1
 
< 0.1%
1.47 4
 
< 0.1%
1.49 2
 
< 0.1%
ValueCountFrequency (%)
551693.65 1
< 0.1%
454343.65 1
< 0.1%
390478.73 1
< 0.1%
387400.22 1
< 0.1%
377661.06 1
< 0.1%
373523.47 1
< 0.1%
347390.14 1
< 0.1%
328589.09 1
< 0.1%
326348.15 1
< 0.1%
321033.23 1
< 0.1%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.1 KiB
conventional
9126 
organic
9123 

Length

Max length12
Median length12
Mean length9.500411
Min length7

Characters and Unicode

Total characters173373
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconventional
2nd rowconventional
3rd rowconventional
4th rowconventional
5th rowconventional

Common Values

ValueCountFrequency (%)
conventional 9126
50.0%
organic 9123
50.0%

Length

2024-02-16T23:27:23.458791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T23:27:23.506128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
conventional 9126
50.0%
organic 9123
50.0%

Most occurring characters

ValueCountFrequency (%)
n 36501
21.1%
o 27375
15.8%
c 18249
10.5%
i 18249
10.5%
a 18249
10.5%
v 9126
 
5.3%
e 9126
 
5.3%
t 9126
 
5.3%
l 9126
 
5.3%
r 9123
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173373
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36501
21.1%
o 27375
15.8%
c 18249
10.5%
i 18249
10.5%
a 18249
10.5%
v 9126
 
5.3%
e 9126
 
5.3%
t 9126
 
5.3%
l 9126
 
5.3%
r 9123
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 173373
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36501
21.1%
o 27375
15.8%
c 18249
10.5%
i 18249
10.5%
a 18249
10.5%
v 9126
 
5.3%
e 9126
 
5.3%
t 9126
 
5.3%
l 9126
 
5.3%
r 9123
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36501
21.1%
o 27375
15.8%
c 18249
10.5%
i 18249
10.5%
a 18249
10.5%
v 9126
 
5.3%
e 9126
 
5.3%
t 9126
 
5.3%
l 9126
 
5.3%
r 9123
 
5.3%

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.1 KiB
2017
5722 
2016
5616 
2015
5615 
2018
1296 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters72996
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2017 5722
31.4%
2016 5616
30.8%
2015 5615
30.8%
2018 1296
 
7.1%

Length

2024-02-16T23:27:23.547864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T23:27:23.596269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2017 5722
31.4%
2016 5616
30.8%
2015 5615
30.8%
2018 1296
 
7.1%

Most occurring characters

ValueCountFrequency (%)
2 18249
25.0%
0 18249
25.0%
1 18249
25.0%
7 5722
 
7.8%
6 5616
 
7.7%
5 5615
 
7.7%
8 1296
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72996
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18249
25.0%
0 18249
25.0%
1 18249
25.0%
7 5722
 
7.8%
6 5616
 
7.7%
5 5615
 
7.7%
8 1296
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 72996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18249
25.0%
0 18249
25.0%
1 18249
25.0%
7 5722
 
7.8%
6 5616
 
7.7%
5 5615
 
7.7%
8 1296
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18249
25.0%
0 18249
25.0%
1 18249
25.0%
7 5722
 
7.8%
6 5616
 
7.7%
5 5615
 
7.7%
8 1296
 
1.8%

region
Text

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size285.1 KiB
2024-02-16T23:27:23.789931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length19
Median length16
Mean length10.295359
Min length4

Characters and Unicode

Total characters187880
Distinct characters45
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbany
2nd rowAlbany
3rd rowAlbany
4th rowAlbany
5th rowAlbany
ValueCountFrequency (%)
albany 338
 
1.9%
denver 338
 
1.9%
midsouth 338
 
1.9%
baltimorewashington 338
 
1.9%
boise 338
 
1.9%
boston 338
 
1.9%
buffalorochester 338
 
1.9%
california 338
 
1.9%
charlotte 338
 
1.9%
chicago 338
 
1.9%
Other values (44) 14869
81.5%
2024-02-16T23:27:24.083002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18590
 
9.9%
o 16559
 
8.8%
e 15198
 
8.1%
n 13182
 
7.0%
t 12841
 
6.8%
i 12165
 
6.5%
r 11154
 
5.9%
l 11154
 
5.9%
s 10475
 
5.6%
h 6084
 
3.2%
Other values (35) 60478
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159162
84.7%
Uppercase Letter 28718
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18590
11.7%
o 16559
10.4%
e 15198
9.5%
n 13182
8.3%
t 12841
8.1%
i 12165
 
7.6%
r 11154
 
7.0%
l 11154
 
7.0%
s 10475
 
6.6%
h 6084
 
3.8%
Other values (13) 31760
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 4394
15.3%
N 2701
 
9.4%
C 2366
 
8.2%
L 2028
 
7.1%
P 1690
 
5.9%
R 1690
 
5.9%
D 1690
 
5.9%
B 1352
 
4.7%
M 1349
 
4.7%
T 1349
 
4.7%
Other values (12) 8109
28.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 187880
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18590
 
9.9%
o 16559
 
8.8%
e 15198
 
8.1%
n 13182
 
7.0%
t 12841
 
6.8%
i 12165
 
6.5%
r 11154
 
5.9%
l 11154
 
5.9%
s 10475
 
5.6%
h 6084
 
3.2%
Other values (35) 60478
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18590
 
9.9%
o 16559
 
8.8%
e 15198
 
8.1%
n 13182
 
7.0%
t 12841
 
6.8%
i 12165
 
6.5%
r 11154
 
5.9%
l 11154
 
5.9%
s 10475
 
5.6%
h 6084
 
3.2%
Other values (35) 60478
32.2%

Interactions

2024-02-16T23:27:21.162232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:17.798052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.435639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.928606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.335040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.701117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.049999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.420062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.776738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.203905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:17.929585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.476674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.975813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.372932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.739800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.090691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.458689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.816647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.242479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.131171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.517618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.046388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.411649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.779201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.133988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.497767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.856289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.280524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.172647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.556914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.108765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.447133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.814860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.172191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.535805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.903146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.315996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.211269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.594524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.145351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.490794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.848684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.212593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.574572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.943918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.356382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.269414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.634341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.181848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.532432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.886564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.252867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.612640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.994703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.398231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.318552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.764422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.221182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.577146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.928084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.296910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.655344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.038566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.442521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.359246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.824098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.260149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.622831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.971607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.339053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.694850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.086206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.483139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.398098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:18.885940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.296863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:19.662996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.010524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.380803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:20.735216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T23:27:21.124540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-16T23:27:21.543868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T23:27:21.771627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
02015-12-271.3364236.621036.7454454.8548.168696.878603.6293.250.0conventional2015Albany
12015-12-201.3554876.98674.2844638.8158.339505.569408.0797.490.0conventional2015Albany
22015-12-130.93118220.22794.70109149.67130.508145.358042.21103.140.0conventional2015Albany
32015-12-061.0878992.151132.0071976.4172.585811.165677.40133.760.0conventional2015Albany
42015-11-291.2851039.60941.4843838.3975.786183.955986.26197.690.0conventional2015Albany
52015-11-221.2655979.781184.2748067.9943.616683.916556.47127.440.0conventional2015Albany
62015-11-150.9983453.761368.9273672.7293.268318.868196.81122.050.0conventional2015Albany
72015-11-080.98109428.33703.75101815.3680.006829.226266.85562.370.0conventional2015Albany
82015-11-011.0299811.421022.1587315.5785.3411388.3611104.53283.830.0conventional2015Albany
92015-10-251.0774338.76842.4064757.44113.008625.928061.47564.450.0conventional2015Albany
DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
22018-03-111.5622128.422162.673194.258.9316762.5716510.32252.250.0organic2018WestTexNewMexico
32018-03-041.5417393.301832.241905.570.0013655.4913401.93253.560.0organic2018WestTexNewMexico
42018-02-251.5718421.241974.262482.650.0013964.3313698.27266.060.0organic2018WestTexNewMexico
52018-02-181.5617597.121892.051928.360.0013776.7113553.53223.180.0organic2018WestTexNewMexico
62018-02-111.5715986.171924.281368.320.0012693.5712437.35256.220.0organic2018WestTexNewMexico
72018-02-041.6317074.832046.961529.200.0013498.6713066.82431.850.0organic2018WestTexNewMexico
82018-01-281.7113888.041191.703431.500.009264.848940.04324.800.0organic2018WestTexNewMexico
92018-01-211.8713766.761191.922452.79727.949394.119351.8042.310.0organic2018WestTexNewMexico
102018-01-141.9316205.221527.632981.04727.0110969.5410919.5450.000.0organic2018WestTexNewMexico
112018-01-071.6217489.582894.772356.13224.5312014.1511988.1426.010.0organic2018WestTexNewMexico